CN115102648A - Multi-band swarm intelligence spectrum sensing method based on Stackelberg game - Google Patents

Multi-band swarm intelligence spectrum sensing method based on Stackelberg game Download PDF

Info

Publication number
CN115102648A
CN115102648A CN202210598013.8A CN202210598013A CN115102648A CN 115102648 A CN115102648 A CN 115102648A CN 202210598013 A CN202210598013 A CN 202210598013A CN 115102648 A CN115102648 A CN 115102648A
Authority
CN
China
Prior art keywords
perception
sensing
secondary user
cooperative
reward
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210598013.8A
Other languages
Chinese (zh)
Other versions
CN115102648B (en
Inventor
朱琦
郭晓敏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202210598013.8A priority Critical patent/CN115102648B/en
Priority to PCT/CN2022/107291 priority patent/WO2023231165A1/en
Publication of CN115102648A publication Critical patent/CN115102648A/en
Application granted granted Critical
Publication of CN115102648B publication Critical patent/CN115102648B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a multi-band crowd-sourcing spectrum sensing method based on a Stackelberg game, which is used for modeling a problem that a sensing demand secondary user pays reward to a cooperative sensing secondary user into a game model, wherein the sensing demand secondary user is a leader layer, and the cooperative sensing secondary user is a subordinate layer. And the perception requirement secondary users issue frequency band perception tasks and initial rewards, each cooperative perception secondary user optimizes the self utility by optimizing perception time and sends perception data to the perception requirement secondary users, and the perception requirement secondary users continuously update the rewards to optimize the utility and obtain final judgment results. The method comprehensively considers the detection probability and the reward in the leader layer game to define the effectiveness of perception-required secondary users, optimizes the reward through the game to obtain the optimal effectiveness, comprehensively considers the detection probability and the perception time in the subordinate layer game to define the effectiveness of cooperative perception secondary users, issues reward optimization perception time according to the perception-required secondary users to obtain the optimal effectiveness, and deduces and proves that the optimization of the perception time has Nash balance.

Description

Multi-band swarm intelligence spectrum sensing method based on Stackelberg game
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a multi-band swarm intelligence spectrum sensing method based on a Stackelberg game.
Background
With the rapid increase of intelligent terminal equipment, spectrum resources are increasingly in short supply, the Cognitive Radio (CR) technology can greatly improve the spectrum utilization rate through spectrum sharing, and spectrum sensing is an important link of the Cognitive Radio technology. The spectrum hole refers to an unoccupied idle frequency band that is legally used by an Authorized User (AU), and the cognitive radio may access a Secondary User (SU) to the spectrum hole, but it is important to detect and confirm whether the authorized User exists through a spectrum sensing technology in order to realize access of idle frequency spectrum resources.
The secondary user spectrum sensing usually adopts an energy detection method, does not need to know the prior knowledge of the authorized user, and judges whether the authorized user exists or not by calculating whether the energy accumulated in the frequency band exceeds a threshold value or not. However, in the face of the influence of adverse factors such as shadow effect, multipath fading, uncertain noise and the like in a wireless environment, the sensing result of a single secondary user on an authorized frequency band is often unreliable, and the cooperative spectrum sensing performance of a plurality of secondary users is superior to that of the single secondary user, so that the cooperative spectrum sensing of the plurality of secondary users is usually adopted. Many researches on multi-user cooperative spectrum sensing are to determine whether a secondary user exists in a non-paid sensing authorized mode, which is unrealistic in life, and because the secondary user consumes computing resources such as time, energy and memory in the sensing process, the multi-user sensing result is more accurate, but the secondary user may not be willing to participate in the spectrum sensing process in a non-paid manner. Therefore, the problem can be effectively solved by introducing an incentive mechanism into spectrum sensing, and the incentive mechanism compensates the cost of secondary users in a reward payment mode and encourages the secondary users to actively participate in cooperative spectrum sensing.
Document [10] proposes a cooperative spectrum sensing algorithm based on SU classification, which introduces an incentive mechanism to encourage more SUs to actively participate in detection, and the algorithm divides secondary users into Ordinary Secondary Users (OSU) and Relay Secondary Users (RSU) according to channel conditions, firstly, each SU decides whether to participate by calculating a utility function, then the OSU sends detected data to nearby RSUs, and then the RSUs send the received data to a fusion center together with local detection data.
The research only aims at single frequency band for spectrum sensing, in an actual system, a plurality of frequency bands are often occupied, and the research does not consider the problem of secondary user sensing cost optimization, so that the sensing cost is high. The invention introduces a crowd sensing technology into spectrum sensing, considers a multi-band scene, and provides a multi-band crowd sensing method based on a Stackelberg game, so that the sensing cost of cooperative sensing secondary users is reduced by optimizing the sensing time.
[10]LI Peijun,HAN Bo,LI Heng,et al.The research of spectrum sensing based on SU classification in cognitive LTE-A network[C]//2019IEEE 3rd Information Technology,Networking,Electronic andAutomation Control Conference,Chengdu,China.IEEE,2019:1917-1921.
Disclosure of Invention
The invention aims to overcome the defect of sensing a single frequency band in the prior art, and provides a multi-band crowd-sourcing frequency spectrum sensing method based on a Stackelberg game.
In order to solve the technical problems, the invention adopts the following technical scheme.
A multi-band swarm intelligence spectrum sensing method based on a Stackelberg game models a problem that a sensing demand secondary user pays reward to a cooperative sensing secondary user as a Stackelberg game system model, wherein the sensing demand secondary user is a leader layer in the game model, and the cooperative sensing secondary user is a subordinate layer in the game model; the scene of the system is a circular area, N cooperative perception sub-users and M perception requirement sub-users are randomly distributed, and M is 2, namely two perception requirement sub-users exist; the set of the secondary users with the perception requirements is represented as
Figure BDA0003668579100000021
The set of cooperative perception secondary users is expressed as
Figure BDA0003668579100000022
The method comprises the following steps:
step1, constructing a subordinate layer optimization problem, and deducing that a Nash equilibrium solution exists in the cooperative perception of the secondary user game: defining a utility function of the cooperative perception secondary user by comprehensively considering the detection probability and the perception time, so that the utility of the cooperative perception secondary user is maximized;
step2, constructing a leader optimization problem: the utility function of the perception requirement secondary user is defined by comprehensively considering the detection probability and the task reward after voting fusion, so that the utility of the perception requirement secondary user is maximized;
step3, constructing a multi-band crowd-sourcing spectrum sensing system model based on the Stackelberg game by the problem that the sensing demand secondary users pay rewards to the cooperative sensing secondary users, wherein the sensing demand secondary users are leader layers in the game model, the cooperative sensing secondary users are subordinate layers, and each cooperative sensing secondary user can sense all frequency bands but only one frequency band;
step4, the perception requirement secondary user issues tasks and initial rewards to the cooperative perception secondary user for the frequency band to be perceived, and the maximum utility of the perception requirement secondary user is initialized
Figure BDA0003668579100000023
Is 0;
step5, calculating the utility of the cooperative perception secondary users under the current reward according to the reward and the perception time, selecting the perception time corresponding to the maximum utility by optimizing the perception time, calculating the corresponding detection probability and cost according to the perception time by the cooperative perception secondary users, and transmitting the data pair (perception time, detection probability, price quoted based on the cost) to the perception demand secondary users;
step6, the perception requirement secondary user sends recruitment willingness and price for payment to the cooperation perception secondary user with high detection probability according to the payment;
step7, if the cooperative perception secondary users are recruited by a plurality of perception requirement secondary users at the same time, obtaining perception task joining with much remuneration by comparing the price selection provided by the perception requirement secondary users;
step8, perceiving the need to calculate the utility at the current reward by the secondary user, if the utility value is higher than
Figure BDA0003668579100000024
The reward is recorded at maximum reward B max Increasing the reward value by Step mu under the limit of (2), issuing a new reward and repeating Step5-Step8 until the utility value error of two adjacent perception demand secondary users is less than delta;
and Step9, taking the reward corresponding to the optimal effectiveness of the perception requirement secondary user obtained in Step8 as the final reward, determining the final perception time according to the reward by the cooperation perception secondary user, uploading perception data to the perception requirement secondary user, and obtaining the final judgment result.
In particular, collaboratively aware secondary users
Figure BDA0003668579100000031
Payment p of ij Comprises the following steps:
Figure BDA0003668579100000032
wherein
Figure BDA0003668579100000033
Representing the detection probability of the sensing frequency band task j of the cooperative sensing secondary user i, B j Represents the perceived demand the reward issued by the secondary user j, T j And representing a cooperative secondary user set participating in the frequency band perception task j.
In particular, cost c of collaboratively perceived secondary users ij Comprises the following steps:
c ij ＝β×t ij +γ×d ij (2)
wherein β and γ represent weighting coefficients, t ij Representing the sensing time d of the sensing frequency band task j of the cooperative sensing secondary user i ij Representing the distance between the cooperative perception secondary user i and the perception requirement secondary user j.
In particular, collaboratively aware secondary users
Figure BDA0003668579100000034
The utility of (A) is as follows:
Figure BDA0003668579100000035
wherein p is ij Representing a reward received by the cooperative perception secondary user i, c ij Represents the cost consumed by the cooperative sensing secondary user i to complete the frequency band sensing task j,
Figure BDA0003668579100000036
representing the detection probability of a task j of a perception frequency band i of a cooperative perception secondary user, B j Representing the reward issued by the perceived need secondary user j, beta and gamma representing weighting coefficients, t ij Representing the sensing time of a sensing frequency band task j of a cooperative sensing secondary user i ij Representing the distance between the cooperative perception secondary user i and the perception requirement secondary user j.
Specifically, the detection probability formula of the cooperative sensing secondary user i sensing frequency band j is as follows:
Figure BDA0003668579100000037
wherein p is f Representing the false alarm probability, gamma, of the secondary user i ij Representing the signal-to-noise ratio, t, of the perceived frequency band j of the secondary user i ij Representing the sensing time, f, of the sensing frequency band j of the secondary user i s Representing the sampling frequency, usually a constant value, the Q function is a complementary cumulative distribution function expressed as:
Figure BDA0003668579100000041
in particular, sensing demand sub-users
Figure BDA0003668579100000042
Perceiving secondary users through recruitment collaboration
Figure BDA0003668579100000043
The utility obtained by completing the sensing task of the corresponding frequency band is represented as:
Figure BDA0003668579100000044
where a represents a weighting coefficient and where,
Figure BDA0003668579100000045
the detection probability of the frequency band j is obtained after voting fusion of the secondary users with the perception requirements,
Figure BDA0003668579100000046
representing the detection probability of a task j of a perception frequency band i of a cooperative perception secondary user, B j Representing the perceived need for a reward issued by secondary user j.
Specifically, the detection probability of the perception task j after voting fusion of the perception requirement secondary users is represented as follows:
Figure BDA0003668579100000047
wherein
Figure BDA0003668579100000048
Representing the false alarm probability of the secondary user i perceiving the frequency band j,
Figure BDA0003668579100000049
representing the detection probability of the secondary user i sensing frequency band j.
Preferably, the sampling frequency of the cooperative sensing sub-user is 10kHz, the false alarm probability is 0.1, the weighting coefficient α is 8, β is 1, and γ is 0.3, the wireless signal transmission considers large-scale fading, the fading coefficient is 4, and the decision threshold value of the voting fusion criterion is N/2.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the method comprises the steps of respectively modeling a perception requirement secondary user and a collaboration perception secondary user into a leader layer and a subordinate layer of a Stackelberg game, obtaining respective optimal strategies of the perception requirement secondary user and the collaboration perception secondary user through the game, optimizing reward in the leader layer game to enable the perception requirement secondary user to have optimal utility, and optimizing perception time in the subordinate layer game to enable the collaboration perception secondary user to have optimal utility.
2. According to the method, spectrum sensing and crowd sensing are combined, a plurality of sensing demand secondary users working in different frequency bands are considered to recruit cooperative sensing secondary users to complete tasks to obtain the use conditions of the different frequency bands, in the scene, one cooperative sensing secondary user can only sense one frequency band at the same time, the cooperative sensing secondary users send sensing results to the sensing demand secondary users, and the sensing demand secondary users fuse the results of the plurality of cooperative sensing secondary users to obtain more accurate sensing results.
3. According to the method, the situation that a plurality of perception requirement secondary users working in different frequency bands need to perceive the different frequency bands is considered, the frequency band perception tasks are issued by the perception requirement secondary users, and the service conditions of the frequency bands obtained by the cooperation perception secondary users are recruited respectively. The cooperative sensing secondary users recruited by each sensing demand secondary user are not determined in advance, but vary with the game process according to the detection probability, sensing time and quotation of the cooperative sensing secondary users.
4. The utility of the perception requirement secondary user is defined as comprehensively considering the detection probability and the reward, the utility of the cooperation perception secondary user is defined as subtracting the cost from the reward, the reward is related to the detection probability, and the cost is related to the perception time and the distance between the cooperation perception secondary user and the perception requirement secondary user.
5. The method considers reverse selection when the cooperative sensing secondary user selects, and when one cooperative sensing secondary user is sent recruitment willingness only by one sensing demand secondary user, the cooperative sensing secondary user completes the sensing task. When one cooperative perception secondary user is sent recruitment willingness by a plurality of perception requirement secondary users at the same time, the secondary user can obtain the task with the largest reward by comparing reward price selections given by the plurality of perception requirement secondary users for joining.
Drawings
FIG. 1 is a flow chart of a method of one embodiment of the present invention.
Fig. 2 is a schematic diagram of a Stackelberg gaming system model according to an embodiment of the present invention.
Detailed Description
The invention discloses a multi-band crowd-sourcing spectrum sensing method based on a Stackelberg game, which models a problem that a sensing demand secondary user pays reward to a cooperative sensing secondary user into a Stackelberg game system model, wherein the sensing demand secondary user is a leader layer in the game model, and the cooperative sensing secondary user is a subordinate layer in the game model. The perception requirement secondary user issues a frequency band perception task and an initial reward, each cooperative perception secondary user enables self utility to be optimal by optimizing perception time and sends perception data to the perception requirement secondary user, the perception requirement secondary user enables the utility to be optimal by continuously updating the reward, and a final judgment result is obtained. In the leader layer game, the method comprehensively considers the detection probability and the reward to define the utility of the perception requirement secondary user, optimizes the reward through the game to obtain the best utility, in the subordinate layer game, the method comprehensively considers the detection probability and the perception time to define the utility of the cooperation perception secondary user, optimizes the perception time according to the reward issued by the perception requirement secondary user to obtain the best utility, and deduces and proves that the optimization of the perception time has nash balance.
The present invention will be described in further detail with reference to the drawings and examples.
Fig. 2 is a schematic diagram of a Stackelberg gaming system model according to an embodiment of the present invention. As shown in fig. 2, the scene of the system is a circular area, N cooperative sensing sub-users and M sensing demand sub-users are randomly distributed, and M is 2 in the present invention, that is, there are two sensing demand sub-users. In this embodiment, the sampling frequency of the cooperative sensing sub-user is 10kHz, the false alarm probability is 0.1, the weighting coefficient α is 8, β is 1, and γ is 0.3, the wireless signal transmission considers large-scale fading, the fading coefficient is 4, and the decision threshold value of the voting fusion criterion is N/2. In order to stimulate the cooperative perception secondary users to complete the perception tasks, the perception requiring secondary users pay a reward to the secondary users providing the perception results.
The present invention has two parts of secondary users in the system model, the first part of secondary users work in different frequency bands separately and want toThe method includes the steps that a sensing task needs to be issued first and then other idle secondary users are recruited to conduct cooperative spectrum sensing to obtain the use condition of a frequency band when the secondary users of an authorized frequency band need to be used under the condition that authorized users are not affected, and a set formed by the secondary users with requirements is called a sensing requirement secondary user set and is represented as a sensing requirement secondary user set
Figure BDA0003668579100000051
The other part is idle secondary users, after receiving the task issued by the perception demand secondary users, the idle secondary users sense the task and upload the sensing result through intelligent equipment carried by the idle secondary users, and a set formed by the idle secondary users is called a cooperative perception secondary user set
Figure BDA0003668579100000061
As shown in fig. 1, the multiband crowd-sourcing spectrum sensing method based on the Stackelberg game of the present invention includes the following steps:
step1, constructing a subordinate layer optimization problem and deducing a Nash equilibrium solution of cooperative perception of secondary user game: and defining a utility function of the cooperative perception secondary user by comprehensively considering the detection probability and the perception time, namely, the optimization problem of the subordinate layer is to maximize the utility of the cooperative perception secondary user.
Cooperative awareness of secondary users
Figure BDA0003668579100000062
The task of frequency band perception can be completed from perception requirement secondary users
Figure BDA0003668579100000063
Where the reward is obtained and the obtained reward is related to its own detection probability, so that the secondary user is perceived cooperatively
Figure BDA0003668579100000064
Reward p of ij Is defined as:
Figure BDA0003668579100000065
wherein
Figure BDA0003668579100000066
Representing the detection probability of a task j of a perception frequency band i of a cooperative perception secondary user, B j Represents the perceived demand the reward issued by the secondary user j, T j And representing a cooperative secondary user set participating in a frequency band perception task j.
Cooperative awareness of secondary users
Figure BDA0003668579100000067
The cost consumed for completing the sensing task comprises the cost consumed by the sensing frequency band, the cost consumed by uploading sensing data, the cost consumed by the sensing frequency band and the sensing time t ij Cost of upload perception data consumption and collaborative perception secondary users
Figure BDA0003668579100000068
And perceiving a demanded secondary user
Figure BDA0003668579100000069
Is related to the distance between them, thus the cost c of the cooperative perception of the secondary users ij Is defined as follows:
c ij ＝β×t ij +γ×d ij (2)
where β and γ represent weighting coefficients, t ij Representing the sensing time of a sensing frequency band task j of a cooperative sensing secondary user i ij And the distance between the cooperative perception secondary user i and the perception requirement secondary user j is represented.
So collaboratively aware secondary users
Figure BDA00036685791000000610
The utility of (c) is defined as:
Figure BDA00036685791000000611
wherein p is ij Representing collaboratively aware secondary users
Figure BDA00036685791000000612
Payment earned, c ij Representing collaboratively aware secondary users
Figure BDA00036685791000000613
The cost of completing the frequency band sensing task,
Figure BDA00036685791000000614
representing the detection probability of a task j of a perception frequency band i of a cooperative perception secondary user, B j Representing the reward issued by the perceived need secondary user j, beta and gamma representing weighting coefficients, t ij Representing the sensing time of a sensing frequency band task j of a cooperative sensing secondary user i ij And the distance between the cooperative perception secondary user i and the perception requirement secondary user j is represented.
For collaboratively aware secondary users
Figure BDA00036685791000000615
In other words, in order to obtain more remuneration, the secondary users need to be perceptually demanded
Figure BDA00036685791000000616
Submitting an optimal detection probability, and assuming that only the sensing time in the detection probability can be sensed by the cooperative sensing secondary user
Figure BDA0003668579100000071
Decided by itself, in order to make the cooperative perception of secondary users
Figure BDA0003668579100000072
Utility-optimal, cooperative-aware secondary users
Figure BDA0003668579100000073
The optimal perception time of the user can be determined through the game, so that the optimal detection probability is obtained, and therefore the subordinate layer collaboratively perceives secondary users
Figure BDA0003668579100000074
Is superior toThe problem is expressed as:
Figure BDA0003668579100000075
in cognitive radio spectrum sensing, a secondary user senses whether a spectrum of an authorized user is used or not through an energy detection method, and a detection probability formula of a sensing frequency band j of a cooperative sensing secondary user i is expressed as follows:
Figure BDA0003668579100000076
wherein p is f Representing the false alarm probability, gamma, of a secondary user i ij Representing the signal-to-noise ratio, t, of the perceived frequency band j of the secondary user i ij Representing the sensing time, f, of the sensing frequency band j of the secondary user i s Representing the sampling frequency, usually a constant value, the Q function is a complementary cumulative distribution function expressed as:
Figure BDA0003668579100000077
in order to make the detection probability of the secondary user have a reference meaning, it is required
Figure BDA0003668579100000078
Namely, it is
Figure BDA0003668579100000079
Order to
Figure BDA00036685791000000710
Then
Figure BDA00036685791000000711
With respect to t ij The first partial derivative of (d) is expressed as:
Figure BDA00036685791000000712
further, U ij With respect to t ij The first partial derivative of (d) is expressed as:
Figure BDA00036685791000000713
further, U ij With respect to t ij The second partial derivative of (d) is expressed as:
Figure BDA00036685791000000714
wherein the content of the first and second substances,
Figure BDA0003668579100000081
because of the task budget B j Time of sensing t ij Sampling frequency f s Probability of detection
Figure BDA0003668579100000082
Signal to noise ratio gamma ij Are all positive values, so the second part of K
Figure BDA0003668579100000083
Less than 0, third fraction
Figure BDA0003668579100000084
Less than 0, and because K < 0, the first part of K
Figure BDA0003668579100000085
Less than 0, so K' is less than 0, and because U is ij With respect to t ij First part of second order partial derivative
Figure BDA0003668579100000086
Greater than 0, indicating that U ij With respect to t ij Second partial derivative of
Figure BDA0003668579100000087
Namely, it is
Figure BDA0003668579100000088
Utility function U of ij Is about t ij There is a unique optimal solution.
Due to U ij With respect to t ij The second partial derivative of (3) is constantly negative, meaning that U is ij With respect to t ij The first partial derivative of (A) is monotonically decreasing, and since K < 0, i.e.
Figure BDA0003668579100000089
So that there are
Figure BDA00036685791000000810
It is assumed that when K is 0, it is obtained
Figure BDA00036685791000000811
Thereby having
Figure BDA00036685791000000812
Namely U ij With respect to t ij There is a positive value for the first partial derivative of (a).
Suppose that when K → - ∞ is reached, t is available ij → infinity, thus there are
Figure BDA0003668579100000091
Since β > 0, when t is ij The time → ∞ of the time,
Figure BDA0003668579100000092
namely U ij With respect to t ij Has a negative value.
Therefore if
Figure BDA0003668579100000093
Is greater than 0, the optimal sensing time is
Figure BDA0003668579100000094
This can be obtained by the following system of equations:
Figure BDA0003668579100000095
if it is
Figure BDA0003668579100000096
Is less than 0, then
Figure BDA00036685791000000918
The perception time corresponding to the time of maximum utility is
Figure BDA0003668579100000097
Therefore, the temperature of the molten metal is controlled,
Figure BDA0003668579100000098
the perceived time game of (1) presents a unique Nash equilibrium solution, i.e.
Figure BDA00036685791000000919
Detecting a probabilistic game presents a unique nash equilibrium solution.
Step2, constructing a leader optimization problem: and defining a utility function of the perception demand secondary user by comprehensively considering the detection probability and the task reward after voting fusion, namely, the optimization problem of the leader layer is to maximize the utility of the perception demand secondary user.
Consider that
Figure BDA0003668579100000099
Utility of and payment issued and
Figure BDA00036685791000000910
sensing the correlation of detection probabilities of respective frequency bands by
Figure BDA00036685791000000911
Issuing a reward may encourage more cooperative awareness secondary users to participate in the awareness. Demand aware secondary user
Figure BDA00036685791000000912
By recruitment
Figure BDA00036685791000000913
The utility obtained by completing the sensing task of the corresponding frequency band is defined as:
Figure BDA00036685791000000914
where a represents a weighting coefficient and where,
Figure BDA00036685791000000915
the detection probability of the frequency band j is obtained after voting fusion of the secondary users with the perception requirements,
Figure BDA00036685791000000916
representing the detection probability of a task j of a perception frequency band i of a cooperative perception secondary user, B j Representing the perceived need for a reward issued by secondary user j. Each perception requirement secondary user adopts a voting fusion criterion to process perception results submitted by a plurality of cooperative perception secondary users, and the detection probability of a perception task j after voting fusion is expressed as:
Figure BDA00036685791000000917
wherein
Figure BDA0003668579100000101
Representing the false alarm probability of the secondary user i perceiving the frequency band j,
Figure BDA0003668579100000102
representing the detection probability of the secondary user i sensing frequency band j. Thus, the leader layer perceives the required secondary users
Figure BDA0003668579100000103
The optimization problem of (a) is expressed as:
Figure BDA0003668579100000104
assuming that the total reward paid by each perceiving need secondary user to the cooperative perceiving secondary user does not exceed B max Then at 0<B j ≤B max Must have an optimal reward
Figure BDA0003668579100000105
The utility function value of the perception demand secondary user is made to be maximum.
Step3, constructing a multi-band crowd-sourcing spectrum sensing system model based on the Stackelberg game for the problem that the perception demand secondary user pays the reward to the cooperation perception secondary user, wherein the perception demand secondary user is a leader layer in the game model, the cooperation perception secondary user is a subordinate layer, and each cooperation perception secondary user can perceive all frequency bands but can perceive only one frequency band;
step4, the perception requirement secondary user issues tasks and initial rewards to the cooperative perception secondary user for the frequency band to be perceived, and the maximum utility of the perception requirement secondary user is initialized
Figure BDA0003668579100000106
Is 0;
step5, calculating the utility of the cooperative perception secondary users under the current reward according to the reward and the perception time, selecting the perception time corresponding to the maximum utility by optimizing the perception time, calculating the corresponding detection probability and cost according to the perception time by the cooperative perception secondary users, and transmitting the data to the perception time, the detection probability and the quotation generated based on the cost) to the perception demand secondary users;
step6, the perception requirement secondary user sends recruitment willingness and price of payment to the cooperation perception secondary user with high detection probability according to the payment;
step7, if the cooperative perception secondary user is recruited by a plurality of perception requirement secondary users at the same time, obtaining perception tasks with much remuneration by comparing the price selections provided by the plurality of perception requirement secondary users to join;
step8 perception of demand Secondary user calculates utility at Current reward if the utility value is higher than
Figure BDA0003668579100000107
The reward is recorded at maximum reward B max Increasing the reward value by the Step size mu under the limit of (1), issuing a new reward and repeating Step5-Step8 until the utility value error of two adjacent perception demand secondary users is less than delta;
and Step9, taking the reward corresponding to the perception requirement secondary user with the optimal utility obtained in Step8 as the final reward, determining the final perception time according to the reward by the cooperation perception secondary user, and uploading perception data to the perception requirement secondary user to obtain the final judgment result.
In summary, the invention provides a multiband crowd-sourcing spectrum sensing method based on the Stackelberg game by combining the crowd-sourcing sensing technology and aiming at a spectrum sensing scene. The method models the problem that a demand-perceiving secondary user pays a reward to a cooperative perception secondary user into a Stackelberg game model, wherein the demand-perceiving secondary user is a leader layer in the game model, and the cooperative perception secondary user is a subordinate layer in the game model. In the leader game, the utility of the perception demand secondary user is defined by comprehensively considering the detection probability and the reward, and the reward is optimized through the game to obtain the best utility; in subordinate layer gaming, the utility of the cooperative perception secondary user is defined by comprehensively considering the detection probability and the perception time, the optimal utility is obtained by optimizing the perception time according to the reward issued by the perception requirement secondary user, and Nash equilibrium exists in the optimization of the perception time.

Claims (8)

1. A multi-band swarm intelligence spectrum sensing method based on a Stackelberg game is characterized in that a problem that a sensing demand secondary user pays reward to a cooperative sensing secondary user is modeled into a Stackelberg game system model, wherein the sensing demand secondary user is a leading layer in the game model,cooperatively perceiving that the secondary user is a subordinate layer in the gaming model; the scene of the system is a circular area, N cooperative perception sub-users and M perception requirement sub-users are randomly distributed, and M is 2, namely two perception requirement sub-users exist; the set of the secondary users with the perception requirements is represented as
Figure FDA0003668579090000011
The set of cooperative perception secondary users is expressed as
Figure FDA0003668579090000012
The method comprises the following steps:
step1, constructing a subordinate layer optimization problem, and deducing that a Nash equilibrium solution exists in the cooperative perception of the secondary user game: defining a utility function of the cooperative perception secondary user by comprehensively considering the detection probability and the perception time, so that the utility of the cooperative perception secondary user is maximized;
step2, constructing a leader optimization problem: the utility function of the perception requirement secondary user is defined by comprehensively considering the detection probability and the task reward after voting fusion, so that the utility of the perception requirement secondary user is maximized;
step3, constructing a multi-band crowd-sourcing spectrum sensing system model based on the Stackelberg game by the problem that the sensing demand secondary users pay rewards to the cooperative sensing secondary users, wherein the sensing demand secondary users are leader layers in the game model, the cooperative sensing secondary users are subordinate layers, and each cooperative sensing secondary user can sense all frequency bands but only one frequency band;
step4, the perception requirement secondary user issues tasks and initial rewards to the cooperative perception secondary user for the frequency band to be perceived, and the maximum utility of the perception requirement secondary user is initialized
Figure FDA0003668579090000013
Is 0;
step5, calculating the utility of the cooperative perception secondary users under the current reward according to the reward and the perception time, selecting the perception time corresponding to the maximum utility by optimizing the perception time, calculating the corresponding detection probability and cost according to the perception time by the cooperative perception secondary users, and transmitting the data pair (perception time, detection probability, price quoted based on the cost) to the perception demand secondary users;
step6, the perception requirement secondary user sends recruitment willingness and price for payment to the cooperation perception secondary user with high detection probability according to the payment;
step7, if the cooperative perception secondary users are recruited by a plurality of perception requirement secondary users at the same time, obtaining perception task joining with much remuneration by comparing the price selection provided by the perception requirement secondary users;
step8, perceiving the need to calculate the utility at the current reward by the secondary user, if the utility value is higher than
Figure FDA0003668579090000014
The reward is recorded at maximum reward B max Increasing the reward value by the Step size mu under the limit of (1), issuing a new reward and repeating Step5-Step8 until the utility value error of two adjacent perception demand secondary users is less than delta;
and Step9, taking the reward corresponding to the perception requirement secondary user with the optimal utility obtained in Step8 as the final reward, determining the final perception time according to the reward by the cooperation perception secondary user, uploading perception data to the perception requirement secondary user, and obtaining the final judgment result.
2. The multi-band swarm intelligence spectrum sensing method based on the Stackelberg game as claimed in claim 1, wherein the cooperative sensing of secondary users
Figure FDA0003668579090000021
Reward p of ij Comprises the following steps:
Figure FDA0003668579090000022
wherein
Figure FDA0003668579090000023
Representing the detection probability of a task j of a perception frequency band i of a cooperative perception secondary user, B j Represents the perceived demand the reward issued by the secondary user j, T j And representing a cooperative secondary user set participating in the frequency band perception task j.
3. The multi-band swarm intelligence spectrum sensing method based on the Stackelberg game as claimed in claim 1, wherein the cost c of the cooperative sensing secondary user ij Comprises the following steps:
c ij ＝β×t ij +γ×d ij (2)
where β and γ represent weighting coefficients, t ij Representing the sensing time of a sensing frequency band task j of a cooperative sensing secondary user i ij Representing the distance between the cooperative perception secondary user i and the perception requirement secondary user j.
4. The multiband crowd-sourcing spectrum sensing method based on Stackelberg game as claimed in claim 1, wherein cooperative sensing of secondary users is performed
Figure FDA0003668579090000024
The utility of (A) is as follows:
Figure FDA0003668579090000025
wherein p is ij Representing a reward received by the cooperative perception secondary user i, c ij Represents the cost consumed by the cooperative sensing secondary user i to complete the frequency band sensing task j,
Figure FDA0003668579090000026
representing the detection probability of a task j of a perception frequency band i of a cooperative perception secondary user, B j Represents the reward issued by the perception requirement secondary user j, beta and gamma represent weighting coefficients, t ij Representing the sensing time of a sensing frequency band task j of a cooperative sensing secondary user i ij Representing the distance between the cooperative perception secondary user i and the perception requirement secondary user j.
5. The multiband crowd-sourcing spectrum sensing method based on the Stackelberg game as claimed in claim 1, wherein the detection probability formula of the cooperative sensing secondary user i sensing band j is:
Figure FDA0003668579090000027
wherein p is f Representing the false alarm probability, gamma, of a secondary user i ij Representing the signal-to-noise ratio, t, of the perceived frequency band j of the secondary user i ij Representing the sensing time, f, of the sensing frequency band j of the secondary user i s Representing the sampling frequency, usually a constant value, the Q function is a complementary cumulative distribution function expressed as:
Figure FDA0003668579090000031
6. the multi-band swarm intelligence spectrum sensing method based on the Stackelberg game as claimed in claim 1, wherein the sensing of the secondary users in need is performed
Figure FDA0003668579090000032
Perceiving secondary users through recruitment collaboration
Figure FDA0003668579090000033
The utility obtained by completing the sensing task of the corresponding frequency band is represented as:
Figure FDA0003668579090000034
where a represents a weighting coefficient and where,
Figure FDA0003668579090000035
the detection probability of the frequency band j is obtained after voting fusion of the secondary users with the perception requirements,
Figure FDA0003668579090000036
representing the detection probability of the sensing frequency band task j of the cooperative sensing secondary user i, B j Representing the perceived need for a reward issued by secondary user j.
7. The multiband crowd-sourcing spectrum sensing method based on the Stackelberg game as claimed in claim 1, wherein the detection probability of the sensing task j after voting fusion of the sensing demand secondary users is represented as:
Figure FDA0003668579090000037
wherein
Figure FDA0003668579090000038
Representing the false alarm probability of the secondary user i perceiving the frequency band j,
Figure FDA0003668579090000039
representing the detection probability of the secondary user i sensing frequency band j.
8. The multiband crowd-sourcing spectrum sensing method based on the Stackelberg game according to any one of claims 1 to 7, wherein the sampling frequency of the cooperative sensing sub-user is 10kHz, the false alarm probability is 0.1, the weighting coefficient α is 8, β is 1, γ is 0.3, the wireless signal transmission considers the large-scale fading, the fading coefficient thereof is 4, and the decision threshold value of the voting fusion criterion is N/2.
CN202210598013.8A 2022-05-30 2022-05-30 Multi-frequency Duan Qunzhi spectrum sensing method based on Stackelberg game Active CN115102648B (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN202210598013.8A CN115102648B (en) 2022-05-30 2022-05-30 Multi-frequency Duan Qunzhi spectrum sensing method based on Stackelberg game
PCT/CN2022/107291 WO2023231165A1 (en) 2022-05-30 2022-07-22 Multi-band crowd-sourcing spectrum sensing method based on stackelberg competition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210598013.8A CN115102648B (en) 2022-05-30 2022-05-30 Multi-frequency Duan Qunzhi spectrum sensing method based on Stackelberg game

Publications (2)

Publication Number Publication Date
CN115102648A true CN115102648A (en) 2022-09-23
CN115102648B CN115102648B (en) 2023-07-25

Family

ID=83288924

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210598013.8A Active CN115102648B (en) 2022-05-30 2022-05-30 Multi-frequency Duan Qunzhi spectrum sensing method based on Stackelberg game

Country Status (2)

Country Link
CN (1) CN115102648B (en)
WO (1) WO2023231165A1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117118547A (en) * 2022-10-24 2023-11-24 湖南科技学院 Dynamic grouping algorithm for optimizing energy efficiency and perception performance in cognitive radio network

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120108276A1 (en) * 2010-10-27 2012-05-03 The Hong Kong University Of Science And Technology Cooperative spectrum sensing in cognitive radio networks
CN104780007A (en) * 2015-04-03 2015-07-15 南京邮电大学 Coalitional game based multi-user collaborative spectrum sensing method
CN106535198A (en) * 2015-09-15 2017-03-22 索尼公司 Spectrum management device and method as well as devices and methods at base station side and user equipment side
CN110149161A (en) * 2019-05-10 2019-08-20 南京邮电大学 A kind of multitask cooperative frequency spectrum sensing method based on Stackelberg game
CN110798273A (en) * 2019-10-21 2020-02-14 南京邮电大学 Cooperative spectrum sensing method based on optimal secondary user utility

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8014809B2 (en) * 2006-12-11 2011-09-06 New Jersey Institute Of Technology Method and system for decentralized power control of a multi-antenna access point using game theory
CN105528248B (en) * 2015-12-04 2019-04-30 北京邮电大学 Intelligent perception incentive mechanism under multitask collaboration application
CN109492887B (en) * 2018-10-25 2021-10-26 浙江工商大学 Mobile crowd sensing and exciting method based on potential game theory

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120108276A1 (en) * 2010-10-27 2012-05-03 The Hong Kong University Of Science And Technology Cooperative spectrum sensing in cognitive radio networks
CN104780007A (en) * 2015-04-03 2015-07-15 南京邮电大学 Coalitional game based multi-user collaborative spectrum sensing method
CN106535198A (en) * 2015-09-15 2017-03-22 索尼公司 Spectrum management device and method as well as devices and methods at base station side and user equipment side
CN110149161A (en) * 2019-05-10 2019-08-20 南京邮电大学 A kind of multitask cooperative frequency spectrum sensing method based on Stackelberg game
CN110798273A (en) * 2019-10-21 2020-02-14 南京邮电大学 Cooperative spectrum sensing method based on optimal secondary user utility

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117118547A (en) * 2022-10-24 2023-11-24 湖南科技学院 Dynamic grouping algorithm for optimizing energy efficiency and perception performance in cognitive radio network

Also Published As

Publication number Publication date
CN115102648B (en) 2023-07-25
WO2023231165A1 (en) 2023-12-07

Similar Documents

Publication Publication Date Title
Kang et al. Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory
CN104579523B (en) Cognition wireless network frequency spectrum perception and the access united optimization method of decision-making
Tang et al. Energy minimization in D2D-assisted cache-enabled Internet of Things: A deep reinforcement learning approach
Boussaïd et al. Hybridizing biogeography-based optimization with differential evolution for optimal power allocation in wireless sensor networks
Huang et al. Efficient workload allocation and user-centric utility maximization for task scheduling in collaborative vehicular edge computing
Lu et al. Optimization of task offloading strategy for mobile edge computing based on multi-agent deep reinforcement learning
CN110798273B (en) Cooperative spectrum sensing method based on optimal secondary user utility
Nishio et al. Estimation of individual device contributions for incentivizing federated learning
CN110149161B (en) Multi-task cooperative spectrum sensing method based on Stackelberg game
CN115102648B (en) Multi-frequency Duan Qunzhi spectrum sensing method based on Stackelberg game
Cao et al. Cognitive radio networks with heterogeneous users: How to procure and price the spectrum?
Wang et al. Interplay between social selection and social influence on physical proximity in friendship formation
CN112261704B (en) Repeated game routing method based on rechargeable wireless sensor network
Fantacci et al. A d2d-aided federated learning scheme with incentive mechanism in 6G networks
Tang et al. A reinforcement learning approach for D2D-assisted cache-enabled HetNets
Zeng et al. How mobile contributors will interact with each other in mobile crowdsourcing with word of mouth mode
CN108631895B (en) Spectrum sensing secondary user excitation method based on Bayesian game
CN110417572B (en) Method for predicting message transfer node based on target node meeting probability
CN116939866A (en) Wireless federal learning efficiency improving method based on collaborative computing and resource allocation joint optimization
Su et al. Dynamic resource allocation in queue-constrained and delay-sensitive vehicular networks
Lai et al. Optimal wireless information and energy transmissions for UAV-enabled cognitive communication systems
Jiang et al. Age of information-based computation offloading and transmission scheduling in mobile edge computing-enabled iot networks
Li et al. Satisfied matching-embedded social Internet of Things for content preference-aware resource allocation in D2D underlaying cellular networks
CN111866952A (en) D2D probabilistic cache placement method based on user group preference concentration difference
Zhang et al. System revenue maximization for offloading decisions in mobile edge computing

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant